mirror of
https://github.com/wassname/stampy-chat.git
synced 2026-09-11 12:50:34 +08:00
fix various broken things, improvements
This commit is contained in:
Fraser
File diff suppressed because one or more lines are too long
@@ -1,300 +0,0 @@
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# # ---------------------------------- web code ----------------------------------
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# import json
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# from http.server import BaseHTTPRequestHandler
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# class handler(BaseHTTPRequestHandler):
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# # post request = calculate factorial of passed number
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# def do_POST(self):
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# self.send_response(200)
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# self.send_header('Content-type', 'application/json')
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# self.end_headers()
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# content_length = int(self.headers['Content-Length'])
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# post_data = self.rfile.read(content_length)
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# data = json.loads(post_data)
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# results = {}
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# for i, link in enumerate(informed_assistant(data['query'])):
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# results[i] = json.dumps(link.__dict__)
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# self.wfile.write(json.dumps(results).encode('utf-8'))
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# # -------------------------------- non-web-code --------------------------------
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# import time
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# import os
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# import openai
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# import requests
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# from typing import List, Dict
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# import openai
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# import tiktoken
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# import asyncio
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# import config
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# from semantic_search import get_top_k_blocks
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# # OpenAI API key
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# try:
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# import config
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# OPENAI_API_KEY = config.OPENAI_API_KEY
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# except ImportError:
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# OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
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# openai.api_key = OPENAI_API_KEY
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# # OpenAI models
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# EMBEDDING_MODEL = "text-embedding-ada-002"
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# COMPLETIONS_MODEL = "gpt-3.5-turbo"
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# # OpenAI parameters
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# LEN_EMBEDDINGS = 1536
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# MAX_LEN_PROMPT = 4095 # This may be 8191, unsure.
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# # Paths
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# from pathlib import Path
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# project_path = Path(__file__).parent.parent.parent
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# PATH_TO_DATA = project_path / "web" / "api" / "data" / "alignment_texts.jsonl" # Path to the dataset .jsonl file.
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# PATH_TO_EMBEDDINGS = project_path / "web" / "api" / "data" / "embeddings.npy" # Path to the saved embeddings (.npy) file.
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# PATH_TO_DATASET = project_path / "web" / "api" / "data" / "dataset.pkl" # Path to the saved dataset (.pkl) file, containing the dataset class object.
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# class Dataset:
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# pass
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# class Block:
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# def __init__(self, title: str, author: str, date: str, url: str, tags: str, text: str):
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# self.title = title
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# self.author = author
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# self.date = date
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# self.url = url
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# self.tags = tags
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# self.text = text
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# MODERATION_ENDPOINT = "https://api.openai.com/v1/moderations"
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# def moderate_query(query: str) -> List[str]:
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# """This function uses the OpenAI Moderation API to check if a query contains any offensive language.
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# Args:
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# query (str): The query to be checked.
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# Raises:
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# Exception: If the API call fails.
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# Returns:
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# List[str]: A list of categories that the query was flagged for.
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# """
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# headers = {"Content-Type": "application/json","Authorization": f"Bearer {OPENAI_API_KEY}"}
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# data = {"input": query}
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# response = requests.post(MODERATION_ENDPOINT, headers=headers, data=json.dumps(data))
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# flagged_categories = []
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# if response.status_code == 200:
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# moderation_results = response.json()
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# flagged = moderation_results['results'][0]['flagged']
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# categories = moderation_results['results'][0]['categories']
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# if flagged:
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# for category, is_flagged in categories.items():
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# if is_flagged:
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# flagged_categories.append(category)
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# else:
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# raise Exception(f"Error: {response.status_code} {response.reason}")
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# return flagged_categories
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# def limit_tokens(text: str, max_tokens: int, encoding_name: str = "cl100k_base") -> str:
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# encoding = tiktoken.get_encoding(encoding_name)
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# tokens = encoding.encode(text)[:max_tokens]
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# return encoding.decode(tokens)
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# def generate_prompt(user_query: str, previous_dialogue: List[Dict[str, str]] = [], blocks: List[Block] = [], mode: str = "standard") -> List[Dict[str, str]]:
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# """
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# This function generates a prompt in messages format for the OpenAI ChatCompletions API.
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# First, it picks a system description using the mode.
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# Second, it adds the previous dialogue to the prompt.
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# Third, it adds an instruction to the prompt based on the mode.
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# Fourth, it adds the context from the top-k most relevant blocks from the Alignment Research Dataset to the prompt.
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# Fifth, it adds the user query to the prompt.
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# Messages take the following format:
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# messages=[
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# {"role": "system", "content": "You are a helpful assistant."},
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# {"role": "user", "content": "Who won the world series in 2020?"},
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# {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
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# {"role": "user", "content": "Where was it played?"}
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# ]
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# Args:
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# user_query (str): The user query.
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# previous_dialogue (List[Dict[str, str]]): The previous dialogue. Defaults to [].
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# blocks (List[Block]): The top-k most relevant blocks from the Alignment Research Dataset. Defaults to [].
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# mode (str): The mode of the assistant. Can be "standard", etc. Defaults to "standard".
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# Returns:
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# List[Dict[str, str]]: The prompt in messages format.
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# """
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# # Initialize prompt
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# prompt = []
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# # Generate system description
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# if mode == "standard":
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# prompt.append({"role": "system", "content": "You are a helpful assistant knowledgeable about AI Alignment and Safety."})
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# # elif mode == "other":
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# else:
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# raise Exception(f"Invalid mode: {mode}")
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# # Add previous dialogue
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# for message in previous_dialogue:
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# prompt.append(message)
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# # Add instruction
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# if mode == "standard":
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# instruction_prompt = "Please answer my question (after the Q:) using the provided context."
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# prompt.append({"role": "assistant", "content": instruction_prompt})
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# # elif mode == "other":
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# else:
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# raise Exception(f"Invalid mode: {mode}")
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# # Add context from top-k blocks
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# if blocks is None:
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# return "Context missing."
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# context_prompt = "Context:\n\n"
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# for i, block in enumerate(blocks):
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# context_prompt += f"[{i}] {block.text}\n\n"
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# context_prompt = context_prompt[:-2]
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# context_prompt = limit_tokens(context_prompt, 2000)
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# prompt.append({"role": "user", "content": f"{context_prompt}"})
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# # Add user query
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# prompt.append({"role": "user", "content": f"Q: {user_query}"})
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# return prompt
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# def normal_completion(prompt: List[Dict[str, str]]) -> str:
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# """
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# This function uses the OpenAI ChatCompletions API to answer a user query.
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# Args:
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# messages (Dict[str, str]): A dictionary containing the system prompt and user prompt, in addition to any previous dialogue.
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# Returns:
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# str: The answer generated by the API.
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# Raises:
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# Exception: If the API call fails.
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# """
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# try:
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# return openai.ChatCompletion.create(
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# model=COMPLETIONS_MODEL,
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# messages=prompt
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# )["choices"][0]["message"]["content"]
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# except Exception as e:
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# print(e)
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# return "I'm sorry, I failed to process your query. Please try again. If the problem persists, please contact the administrator."
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# async def stream_completion(prompt: List[Dict[str, str]], stream_delay: float = 0.1) -> str:
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# """
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# This function uses the OpenAI ChatCompletions API to answer a user query, streaming the response.
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# Args:
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# messages (Dict[str, str]): A dictionary containing the system prompt and user prompt, in addition to any previous dialogue.
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# Returns:
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# str: The answer generated by the API.
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# Raises:
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# Exception: If the API call fails.
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# """
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# try:
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# async for part in await openai.ChatCompletion.acreate(
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# model=COMPLETIONS_MODEL,
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# messages=prompt,
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# stream=True
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# ):
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# finish_reason = part["choices"][0]["finish_reason"]
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# if "content" in part["choices"][0]["delta"]:
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# content = part["choices"][0]["delta"]["content"]
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# yield content
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# elif finish_reason:
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# print(f"Stream finished: {finish_reason}")
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# break
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# except Exception as e:
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# print(e)
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# response = "I'm sorry, I failed to process your query. Please try again. If the problem persists, please contact the administrator."
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# for word in response.split():
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# time.sleep(stream_delay)
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# yield f"{word} "
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# def informed_assistant(user_query: str, previous_dialogue: List[Dict[str, str]] = [], k: str = 10, mode: str = "standard", HyDE: bool = False, stream: bool = True, stream_delay: float = 0.1) -> str:
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# """
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# This function uses the OpenAI ChatCompletions API to answer a user query.
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# It first checks if the query is offensive, and if so, raises an exception.
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# Then, it finds the top-k most relevant blocks from the Alignment Research Dataset and uses them as context for the ChatCompletions API.
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# It uses the blocks to generate a prompt for the ChatCompletions API.
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# Finally, it uses the ChatCompletions API to generate an answer to the user query.
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# Args:
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# user_query (str): The user query.
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# previous_dialogue (List[Dict[str, str]]): The previous dialogue. Defaults to [].
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# k (str): The number of blocks to use as context.
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# mode (str): The mode to use for the ChatCompletions API. Defaults to "standard".
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# HyDE (bool): Whether to use the HyDE technique for semantic search. This makes search slower, but better. Defaults to False.
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# stream (bool): Whether to stream the results from the ChatCompletions API. Defaults to True.
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# stream_delay (float): The delay between each word in the streamed response when streaming a hard-coded response. Defaults to 0.1.
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# Returns:
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# str: The answer to the user query.
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# Raises:
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# Exception: If the query is offensive.
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# """
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# # 1. Check if the query is offensive
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# flagged_categories: List[str] = moderate_query(user_query)
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# if len(flagged_categories) > 0:
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# response = f"Your query contains offensive language. Please try again."
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# if stream:
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# for word in response.split():
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# time.sleep(stream_delay)
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# yield f"{word} "
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# else:
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# return response
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# # 2. Find the top-k most relevant blocks from the Alignment Research Dataset
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# top_k_blocks: List[Block] = get_top_k_blocks(user_query, k, HyDE)
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# # 3. Generate a prompt for the ChatCompletions API
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# prompt: List[Dict[str, str]] = generate_prompt(user_query, previous_dialogue, top_k_blocks, mode)
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# # 4. Use the top-k most relevant blocks as context for the ChatCompletions API, and generate an answer to the user query
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# if stream:
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# return stream_completion(prompt)
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# else:
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# return normal_completion(prompt)
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# if __name__ == "__main__":
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# # Test the question answering function
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# user_query = "Within the area of mitigating AI risk, there are several broad classes of action being taken. What does Technical safety research focus on?"
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# previous_dialogue = [
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# {"role": "assistant", "content": "Hi! I know all about AI Alignment. Ask me a question!"},
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# ]
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# k = 10
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# mode = "standard"
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# HyDE = True
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# stream = False # Doesn't quite work yet
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# print(asyncio.run(informed_assistant(user_query, previous_dialogue, k, mode, HyDE, stream)))
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# # if stream:
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# # for part in informed_assistant(user_query, previous_dialogue, k, mode, HyDE, stream):
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# # print(part, end="")
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# # else:
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# # print(informed_assistant(user_query, previous_dialogue, k, mode, HyDE, stream))
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+34
-40
@@ -1,3 +1,37 @@
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# ---------------------------------- web code ----------------------------------
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import json
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import dataclasses
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from http.server import BaseHTTPRequestHandler
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@dataclasses.dataclass
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class Block:
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title: str
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author: str
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date: str
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url: str
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tags: str
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text: str
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class Encoder(json.JSONEncoder):
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def default(self, o):
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return dataclasses.asdict(o) if dataclasses.is_dataclass(o) else super().default(o)
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class handler(BaseHTTPRequestHandler):
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def do_POST(self):
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self.send_response(200)
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self.send_header('Content-type', 'application/json')
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self.end_headers()
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content_length = int(self.headers['Content-Length'])
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post_data = self.rfile.read(content_length)
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data = json.loads(post_data)
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self.wfile.write(json.dumps(get_top_k_blocks(data['query']), cls = Encoder).encode('utf-8'))
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# -------------------------------- non-web-code --------------------------------
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import time
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import os
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@@ -48,15 +82,6 @@ class Dataset:
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self.info_types = dataset_dict['info_types']
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self.embeddings = np.array(dataset_dict['embeddings'])
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class Block:
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def __init__(self, title: str, author: str, date: str, url: str, tags: str, text: str):
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self.title = title
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self.author = author
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self.date = date
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self.url = url
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self.tags = tags
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self.text = text
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def get_embedding(text: str) -> np.ndarray:
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"""Get the embedding for a given text. The function will retry with exponential backoff if the API rate limit is reached, up to 4 times.
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@@ -129,34 +154,3 @@ def get_top_k_blocks(user_query: str, k: int = 10, HyDE: bool = False) -> List[B
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return blocks
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# ---------------------------------- web code ----------------------------------
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from http.server import BaseHTTPRequestHandler
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class handler(BaseHTTPRequestHandler):
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def do_POST(self):
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self.send_response(200)
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self.send_header('Content-type', 'application/json')
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self.end_headers()
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content_length = int(self.headers['Content-Length'])
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post_data = self.rfile.read(content_length)
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data = json.loads(post_data)
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results = {}
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query = data['query']
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if 'k' in data:
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k = data['k']
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else:
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k=10
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if 'HyDE' in data:
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HyDE = data['HyDE']
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else:
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HyDE = False
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for i, block in enumerate(get_top_k_blocks(query, k=k, HyDE=HyDE)):
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results[i] = json.dumps(block.__dict__)
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self.wfile.write(json.dumps(results).encode('utf-8'))
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+24
-14
@@ -39,10 +39,19 @@ const Home: NextPage = () => {
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// them back from OpenAI - I think we can just do this with a websocket, which
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// shouldn't be too much harder.
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type SemanticEntry = {
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title: string;
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author: string;
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date: string;
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url: string;
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tags: string;
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text: string;
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};
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const SearchBox: React.FC = () => {
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const [query, setQuery] = useState("");
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const [results, setResults] = useState<{title: string, author: string, date: string, url: string, tags: string, text: string}[]>([]);
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const [results, setResults] = useState<SemanticEntry[] | string>([]);
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const [loading, setLoading] = useState(false);
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const semantic_search = async (query: String) => {
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@@ -55,19 +64,18 @@ const SearchBox: React.FC = () => {
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body: JSON.stringify({query: query}),
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})
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if (!res.ok) {
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setLoading(false);
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return "load failure: " + res.status;
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}
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||||
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const data = await res.json();
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||||
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setLoading(false);
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||||
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// data looks like
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// { 0: "{'title': 'First Title', 'author': 'Bob Miles', 'date': 'March 1st, 2023', 'url': 'https://example.com', 'tags': ['tag1', 'tag2'], 'text': 'This is the content of the article'}",
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// 1: "{'title': 'Second Title', 'author': 'Frank Ocean', 'date': 'March 6th, 2023', 'url': 'https://ai.com', 'tags': ['tag3', 'tag4'], 'text': 'This is the content of the article'}"
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||||
// }
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// so we need to convert it to a list of objects
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return Object.keys(data).map((key) => JSON.parse(data[key])) || [{title: "error", author: "error", date: "error", url: "error", tags: ["error"], text: "error"}];
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return data;
|
||||
};
|
||||
|
||||
|
||||
|
||||
return (
|
||||
<>
|
||||
<form className="flex mb-2" onSubmit={async (e) => { // store in a form so that <enter> submits
|
||||
@@ -86,15 +94,17 @@ const SearchBox: React.FC = () => {
|
||||
</button>
|
||||
</form>
|
||||
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||||
{loading ? <p>loading...</p> : ( // display results in list
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||||
{
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||||
loading ? <p>loading...</p> :
|
||||
typeof results === "string" ? <p className="text-red-500">{results}</p> :
|
||||
<ul>
|
||||
{results.map((result) => (
|
||||
<li key={result.url} className="my-1">
|
||||
{results.map((result, i) => (
|
||||
<li key={i} className="my-3">
|
||||
<a href={result.url}>{result.text}</a>
|
||||
</li>
|
||||
))}
|
||||
</ul>
|
||||
)}
|
||||
}
|
||||
</>
|
||||
);
|
||||
};
|
||||
|
||||
Reference in New Issue
Block a user